On new emerging concepts of petroleum digital ecosystem

Dreher

Rudra

2016

Environ Model Assess

Self Cite

Effective use of historical volumes of heterogeneous and multidimensional data is a major challenge, especially projects associated with potential applications of carbon emission ecosystems. Data science in these applications becomes tedious when such varied data are accumulated and or distributed in multiple domains. Design, development, and implementation of sustainable geological storages are crucial for managing carbon dioxide (CO 2 ) emissions and its modeling process. The purpose of the research is to address major challenges and how best a robust Bontology-based multidimensional data warehousing and mining^approach can resolve issues associated with carbon ecosystems. The conceptualized relationships deduced among multiple domains, integration of domain ontologies, data mining, visualization, and interpretation artefacts are highlights of the study. Several data, plot, and map views are extracted from metadata storage for interpreting new knowledge on carbon emissions. Statistical mining models describe data attributes' correlations, patterns, and trends that can help in predicting future forecast of CO 2 emissions worldwide.

Section: Data Structuring Methodologiesmentioning

confidence: 99%

On a Holistic Modeling Approach for Managing Carbon Emission Ecosystems

Dreher

Rudra

2016

Environ Model Assess

Self Cite

“…Data mining and interpretation of interesting patterns hidden in trillions of seismic and other exploration data are critical goals of geo-informatics. The goals cover identification of useful reservoir bearing structures from petroleum ecosystems [5].…”

Section: Petroleum Data Managemnet and Knowledge Discoverymentioning

confidence: 99%

“…A comprehensive framework is discussed in [1] and [5] in different petroleum ecosystems scenarios. A conceptual framework of PO, as discussed in Fig.…”

Section: Po Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Big-data integration methodologies for effective management and data mining of petroleum digital ecosystems

2013 7th IEEE International Conference on Digital Ecosystems and Technologies (DEST)

Dreher

2013

Petroleum industries' big data characterize heterogeneity and they are often multidimensional in nature. In the recent past, explorers narrate petroleum system, as an ecosystem, in which elements and processes are constantly interacted and communicated each other. Exploration is one of the key super-type data dimensions of petroleum ecosystem, (including seismic dimension), exhibiting high degree of heterogeneity, sequence identity and structural similarity; this is especially the case for, elements and processes that are unique to petroleum systems of South East Asia. Existing approaches of petroleum data organizations have limitations in capturing and integrating petroleum systems data. An alternative method uses ontologies and does not rely on keywords or similarity metrics. The conceptual framework of petroleum ontology (PO) is to promote reuse of concepts and a set of algebraic operators for querying petroleum ontology instances. This ontology-based finegrained multidimensional data structuring adapts to warehouse metadata modeling. The data integration process facilitates to metadata models, which are deduced for Indonesian sedimentary basins, and is useful for data mining and subsequent data interpretation including geological knowledge mapping.

“…1, there may be many bench systems associated with opencast and geomorphic systems, in which a sustainable management is needed through increased understanding of the systems' connectivity in geographically spread unstructured data sources. For exploring the systems' connectivity, the domain ontologies (Aitken andReid, 2000 andNimmagadda andDreher, 2012) are built with likely development of an ontology-based data warehouse repository, an integrated approach. Various surveys-mines-permits' data sources are considered in the multidimensional modelling process.…”

Section: Introductionmentioning

confidence: 99%

On Digital Opencast Mining Ecosystems (DOME) and Knowledge Management - a Big Data Perspective

ASEG Extended Abstracts

Veenaikar

Reiners

2018

SUMMARYMany opencast mines inhabit thousands of square km area, which are productive and commercial Australia wide. Hundreds of volumes and varieties of data dimensions and facts exist in the opencast mining areas. The data sources linked with various opencast mines are often heterogeneous and multidimensional. Data modelling is challenging in a Big Data scale, at times precluding the data integration process. The mineralization connected to opencast mines occurs in shafts, pit slopes, ramps and benches with varying geometries and configurations in large-scale geographic and periodic dimensions. The limits of the mineralization at places are either unknown and or ambiguously interpreted. The Big Data, in the context of the Australian mining industry, are due to the explosive growth of data sources and their uncontrolled management in many national and multinational companies. New knowledge is required for interpreting new opencast mining areas and their mineralization. For sustainable production, the knowledge of the connectivity between mineralization and its associated opencast mines is constrained. We propose an empirical modelling, analysing hundreds of attribute dimensions and fact instances of geological and geophysical vintages in the mining areas. Different data constructs and models are built for logical metadata, accommodating it in a multidimensional warehouse repository, as a DOME solution. It is an innovative solution to the mining industry's Big Data problem including the opencast mine planning and design, adding values to the existing domain knowledge with new interpretations. Various geological events attributed to the interpretation and distribution of mineralization are useful for the opencast mine managers.